Add RunwayLibProtectionLevels package

.github/workflows/CI.yml

@@ -40,6 +40,33 @@ jobs:
         env:
           GROUP: ${{ matrix.group }}
 
+  test-protection-levels:
+    name: Protection Levels - Julia ${{ matrix.version }}
+    runs-on: ${{ vars.USE_SELF_HOSTED == 'true' && 'self-hosted' || 'ubuntu-latest' }}
+    timeout-minutes: 60
+    permissions:
+      actions: write
+      contents: read
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - '1'
+          - '1.12'
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.version }}
+      - uses: julia-actions/cache@v2
+      - name: Test RunwayLibProtectionLevels
+        run: |
+          julia --project=libs/RunwayLibProtectionLevels -e '
+            using Pkg
+            Pkg.instantiate()
+            Pkg.test()
+          '
+
   python-smoke-test:
     name: Python Interface Smoke Test - Julia ${{ matrix.version }}
     runs-on: ubuntu-latest
@@ -71,3 +98,21 @@ jobs:
         run: |
           export UV_PYTHON="$MAMBA_ROOT_PREFIX/envs/test/bin/python"
           uv run smoke_test.py  # installs env automatically
+
+  pluto-notebooks:
+    name: Pluto Notebooks
+    runs-on: ubuntu-latest
+    timeout-minutes: 60
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: '1.13'
+          include-all-prereleases: true
+      - uses: julia-actions/cache@v2
+      - name: Install Pluto
+        run: julia -e 'using Pkg; Pkg.add("Pluto")'
+      - name: Run Pluto notebook tests
+        run: julia test/pluto_notebooks/run_notebook_tests.jl
+        env:
+          RUNWAYLIB_PKGDIR: ${{ github.workspace }}

Project.toml

@@ -4,11 +4,12 @@ version = "0.1.0"
 authors = ["Romeo Valentin <romeov@stanford.edu>"]
 
 [workspace]
-projects = ["test", "benchmark", "juliac", "docs"]
+projects = ["test", "benchmark", "juliac", "docs", "libs/RunwayLibProtectionLevels"]
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+BracketingNonlinearSolve = "70df07ce-3d50-431d-a3e7-ca6ddb60ac1e"
 CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
@@ -22,9 +23,12 @@ LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 Moshi = "2e0e35c7-a2e4-4343-998d-7ef72827ed2d"
 NonlinearSolveBase = "be0214bd-f91f-a760-ac4e-3421ce2b2da0"
 NonlinearSolveFirstOrder = "5959db7a-ea39-4486-b5fe-2dd0bf03d60d"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 Polyester = "f517fe37-dbe3-4b94-8317-1923a5111588"
 PolyesterWeave = "1d0040c9-8b98-4ee7-8388-3f51789ca0ad"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProbabilisticParameterEstimators = "0098cf3f-3e1e-4f11-b01c-dd9dec1b3374"
 Rotations = "6038ab10-8711-5258-84ad-4b1120ba62dc"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
@@ -44,6 +48,7 @@ PythonCallExt = "PythonCall"
 [compat]
 ADTypes = "1.16.0"
 BenchmarkTools = "1.6.0"
+BracketingNonlinearSolve = "1.5.0"
 CEnum = "0.5.0"
 CSV = "0.10"
 DiffEqBase = "^6.179.0"
@@ -57,9 +62,12 @@ LinearSolve = "3.25.0"
 Moshi = "0.3.7"
 NonlinearSolveBase = "1.13.0,2"
 NonlinearSolveFirstOrder = "1.8.0"
+Optimization = "5.4.0"
+OptimizationOptimJL = "0.4.9"
 Polyester = "0.7.18"
 PolyesterWeave = "0.2.2"
 PrecompileTools = "1"
+Printf = "1.11.0"
 ProbabilisticParameterEstimators = "0.8.3"
 PythonCall = "0.9"
 Rotations = "1"

libs/RunwayLibProtectionLevels/Project.toml

@@ -0,0 +1,28 @@
+name = "RunwayLibProtectionLevels"
+uuid = "e8295940-cb7e-45e5-ae62-4728616c04bd"
+version = "0.1.0"
+authors = ["Romeo Valentin <romeov@stanford.edu>"]
+
+[deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
+BracketingNonlinearSolve = "70df07ce-3d50-431d-a3e7-ca6ddb60ac1e"
+Chairmarks = "0ca39b1e-fe0b-4e98-acfc-b1656634c4de"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+NonlinearSolveFirstOrder = "5959db7a-ea39-4486-b5fe-2dd0bf03d60d"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationIpopt = "43fad042-7963-4b32-ab19-e2a4f9a67124"
+OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Rotations = "6038ab10-8711-5258-84ad-4b1120ba62dc"
+RunwayLib = "c31d23ff-e9b5-4186-8ef8-ce4d62614c67"
+SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+
+[compat]
+Chairmarks = "1.3.1"
+OptimizationIpopt = "1.1.0"
+julia = "1.11"

libs/RunwayLibProtectionLevels/benchmark/benchmarks.jl

@@ -0,0 +1,45 @@
+using RunwayLibProtectionLevels
+using RunwayLib
+using Chairmarks
+using Unitful, Unitful.DefaultSymbols
+using StaticArrays
+using Rotations: RotZYX
+using LinearAlgebra
+using Random
+
+# --- Setup: representative scenario ---
+rng = MersenneTwister(42)
+runway_corners = SA[
+    WorldPoint(0.0m, -25.0m, 0.0m),
+    WorldPoint(0.0m, 25.0m, 0.0m),
+    WorldPoint(1500.0m, -25.0m, 0.0m),
+    WorldPoint(1500.0m, 25.0m, 0.0m),
+]
+px_std = sqrt(2.0)
+noise_cov = Diagonal(px_std^2 * ones(8))
+cam_pos = WorldPoint(-2500.0m, 10.0m, 250.0m)
+cam_rot = RotZYX(0.02, 0.05, 0.01)
+clean_obs = [project(cam_pos, cam_rot, c) for c in runway_corners]
+noisy_obs = clean_obs .+ [ProjectionPoint(px_std * randn(rng, 2)RunwayLib.px) for _ in clean_obs]
+
+# Warmup
+compute_zero_fault_protection_level_grad(runway_corners, noisy_obs, noise_cov, cam_rot; alpha_idx=1, prob=0.01)
+
+# --- Benchmarks ---
+println("PL both directions (along-track):")
+display(@be compute_zero_fault_protection_level_grad(
+    runway_corners, noisy_obs, noise_cov, cam_rot;
+    alpha_idx=1, direction=0, prob=0.01,
+) evals=1 samples=20 seconds=60)
+
+println("\nPL single direction (along-track, hi):")
+display(@be compute_zero_fault_protection_level_grad(
+    runway_corners, noisy_obs, noise_cov, cam_rot;
+    alpha_idx=1, direction=1, prob=0.01,
+) evals=1 samples=20 seconds=60)
+
+println("\nPL both directions (cross-track):")
+display(@be compute_zero_fault_protection_level_grad(
+    runway_corners, noisy_obs, noise_cov, cam_rot;
+    alpha_idx=2, direction=0, prob=0.01,
+) evals=1 samples=20 seconds=60)